Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "143" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460008 | RF_maintenance | 100.00% | 92.11% | 100.00% | 0.00% | - | - | 15.345234 | 18.492904 | 14.051937 | 15.282599 | 6.156066 | 7.724189 | 4.206563 | 5.638860 | 0.1261 | 0.0330 | 0.0722 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 98.97% | 100.00% | 0.00% | - | - | 11.328995 | 13.862043 | 11.001428 | 11.951281 | 5.500872 | 7.200084 | 0.987078 | 2.406151 | 0.1107 | 0.0308 | 0.0611 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 98.91% | 99.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3866 | 0.3616 | 0.2837 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.724569 | 11.736201 | 9.395949 | 10.105374 | 7.382223 | 10.238835 | 0.244660 | 1.620620 | 0.0940 | 0.0293 | 0.0500 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 99.95% | 100.00% | 0.00% | - | - | 10.681797 | 12.780584 | 9.955796 | 10.866631 | 7.180197 | 9.583013 | 1.000721 | 2.662670 | 0.1013 | 0.0306 | 0.0540 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.917890 | 13.735999 | 12.496926 | 13.312289 | 6.749671 | 9.215735 | 0.193702 | 1.251763 | 0.0978 | 0.0297 | 0.0514 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.040499 | 13.921354 | 11.618993 | 12.515307 | 7.404236 | 9.493984 | 0.094594 | 1.023508 | 0.0981 | 0.0326 | 0.0480 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 99.95% | 100.00% | 0.00% | - | - | 11.490903 | 13.527284 | 10.043758 | 10.955631 | 7.330095 | 9.559735 | 0.283020 | 1.143262 | 0.0903 | 0.0300 | 0.0443 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 98.02% | 100.00% | 0.00% | - | - | 12.745323 | 12.598455 | 9.347395 | 10.160915 | 9.743567 | 10.933683 | 0.674128 | 2.482466 | 0.0620 | 0.0269 | 0.0247 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.564125 | 15.768363 | 9.902520 | 10.753184 | 8.662094 | 10.764823 | -0.130430 | 0.718235 | 0.0900 | 0.0298 | 0.0469 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.542706 | -0.922843 | -1.291194 | -0.990313 | -0.087026 | 2.229106 | 0.953966 | 0.508207 | 0.6436 | 0.6515 | 0.3719 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.595839 | -0.928895 | -0.971959 | -0.645991 | -0.270344 | 1.663620 | -0.264073 | 0.472368 | 0.6400 | 0.6492 | 0.3737 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.613557 | -1.444982 | -1.375626 | -0.999803 | 0.067056 | 1.679321 | -0.466096 | -0.482206 | 0.6422 | 0.6536 | 0.3696 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.274535 | -0.580630 | -1.345202 | -0.409856 | -0.431798 | 0.698996 | -0.386303 | -0.429979 | 0.6493 | 0.6592 | 0.3683 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.485042 | -1.081231 | -1.491356 | -0.721897 | 1.021201 | 0.653070 | -0.160608 | -0.840982 | 0.6701 | 0.6813 | 0.3220 | nan | nan |
| 2459985 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.351680 | -0.674629 | -1.047241 | -0.248806 | 0.206079 | 2.910099 | 1.279445 | 0.904266 | 0.6502 | 0.6584 | 0.3748 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.048267 | -0.505061 | -1.014882 | 1.060399 | -0.016512 | 0.730838 | 0.421292 | 1.217806 | 0.6617 | 0.6648 | 0.3450 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.680912 | -0.798807 | -1.396845 | -0.808838 | -0.087483 | 0.169959 | -0.744559 | -1.170921 | 0.6756 | 0.6935 | 0.3120 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.759973 | -0.956911 | -1.098747 | -0.621492 | 0.168921 | 1.552350 | -0.425936 | -0.709725 | 0.7306 | 0.7288 | 0.2686 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.672379 | -0.817905 | -1.420221 | -1.444661 | 0.822841 | 20.987213 | -0.424065 | -0.296479 | 0.6525 | 0.6602 | 0.3688 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.064668 | -0.437236 | -0.873415 | 0.891642 | -0.700396 | 2.742000 | -1.120418 | 0.338259 | 0.6926 | 0.6937 | 0.2997 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.349526 | -0.238584 | -0.710680 | 1.091391 | -0.452390 | 4.930280 | -0.862361 | -1.523604 | 0.6469 | 0.6592 | 0.3724 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.267606 | -0.173129 | -0.668600 | 1.261583 | -0.297056 | 5.074263 | -0.678148 | -1.719754 | 0.6480 | 0.6580 | 0.3783 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.271027 | -0.081811 | -0.755024 | 0.834501 | 0.096400 | 2.208843 | -0.587333 | -1.505360 | 0.6142 | 0.6226 | 0.3427 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.281002 | -0.108416 | -0.819113 | 1.238485 | -0.232641 | 7.365041 | -0.772176 | -1.297722 | 0.6566 | 0.6643 | 0.3666 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 18.492904 | 18.492904 | 15.345234 | 15.282599 | 14.051937 | 7.724189 | 6.156066 | 5.638860 | 4.206563 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 13.862043 | 11.328995 | 13.862043 | 11.001428 | 11.951281 | 5.500872 | 7.200084 | 0.987078 | 2.406151 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 11.736201 | 9.724569 | 11.736201 | 9.395949 | 10.105374 | 7.382223 | 10.238835 | 0.244660 | 1.620620 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 12.780584 | 10.681797 | 12.780584 | 9.955796 | 10.866631 | 7.180197 | 9.583013 | 1.000721 | 2.662670 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 13.735999 | 11.917890 | 13.735999 | 12.496926 | 13.312289 | 6.749671 | 9.215735 | 0.193702 | 1.251763 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 13.921354 | 12.040499 | 13.921354 | 11.618993 | 12.515307 | 7.404236 | 9.493984 | 0.094594 | 1.023508 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 13.527284 | 11.490903 | 13.527284 | 10.043758 | 10.955631 | 7.330095 | 9.559735 | 0.283020 | 1.143262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | ee Shape | 12.745323 | 12.745323 | 12.598455 | 9.347395 | 10.160915 | 9.743567 | 10.933683 | 0.674128 | 2.482466 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Shape | 15.768363 | 13.564125 | 15.768363 | 9.902520 | 10.753184 | 8.662094 | 10.764823 | -0.130430 | 0.718235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | RF_maintenance | nn Temporal Variability | 2.229106 | -0.922843 | -0.542706 | -0.990313 | -1.291194 | 2.229106 | -0.087026 | 0.508207 | 0.953966 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 1.663620 | -0.928895 | -0.595839 | -0.645991 | -0.971959 | 1.663620 | -0.270344 | 0.472368 | -0.264073 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 1.679321 | -1.444982 | -0.613557 | -0.999803 | -1.375626 | 1.679321 | 0.067056 | -0.482206 | -0.466096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 0.698996 | -0.274535 | -0.580630 | -1.345202 | -0.409856 | -0.431798 | 0.698996 | -0.386303 | -0.429979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | ee Temporal Variability | 1.021201 | -1.081231 | -0.485042 | -0.721897 | -1.491356 | 0.653070 | 1.021201 | -0.840982 | -0.160608 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 2.910099 | -0.674629 | -0.351680 | -0.248806 | -1.047241 | 2.910099 | 0.206079 | 0.904266 | 1.279445 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Discontinuties | 1.217806 | 0.048267 | -0.505061 | -1.014882 | 1.060399 | -0.016512 | 0.730838 | 0.421292 | 1.217806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 0.169959 | -0.680912 | -0.798807 | -1.396845 | -0.808838 | -0.087483 | 0.169959 | -0.744559 | -1.170921 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 1.552350 | -0.759973 | -0.956911 | -1.098747 | -0.621492 | 0.168921 | 1.552350 | -0.425936 | -0.709725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 20.987213 | -0.817905 | -0.672379 | -1.444661 | -1.420221 | 20.987213 | 0.822841 | -0.296479 | -0.424065 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 2.742000 | -0.437236 | -1.064668 | 0.891642 | -0.873415 | 2.742000 | -0.700396 | 0.338259 | -1.120418 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 4.930280 | -1.349526 | -0.238584 | -0.710680 | 1.091391 | -0.452390 | 4.930280 | -0.862361 | -1.523604 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 5.074263 | -0.173129 | -1.267606 | 1.261583 | -0.668600 | 5.074263 | -0.297056 | -1.719754 | -0.678148 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 2.208843 | -1.271027 | -0.081811 | -0.755024 | 0.834501 | 0.096400 | 2.208843 | -0.587333 | -1.505360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 143 | N14 | digital_ok | nn Temporal Variability | 7.365041 | -0.108416 | -1.281002 | 1.238485 | -0.819113 | 7.365041 | -0.232641 | -1.297722 | -0.772176 |